{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：17/24 | 概率：70.83%\n",
      "最近 3 年上涨月数/总月数：24/36 | 概率：66.67%\n",
      "最近 5 年上涨月数/总月数：36/60 | 概率：60.00%\n",
      "最近 8 年上涨月数/总月数：55/96 | 概率：57.29%\n",
      "最近 10 年上涨月数/总月数：68/120 | 概率：56.67%\n",
      "最近 15 年上涨月数/总月数：101/180 | 概率：56.11%\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = symbol_code = \"黄金\"\n",
    "year_spans = [2, 3, 5, 8, 10, 15]  # 不同周期\n",
    "\n",
    "# ========== 获取完整数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "df = ak.futures_main_sina(symbol=\"AU0\", start_date=full_start_date, end_date=end_date)\n",
    "df[\"日期\"] = pd.to_datetime(df[\"日期\"])\n",
    "df = df.sort_values(\"日期\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 收集每个周期的数据 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"日期\"] >= start_cut].copy()\n",
    "\n",
    "    # 月份字段\n",
    "    df_cut[\"月份\"] = df_cut[\"日期\"].dt.month\n",
    "\n",
    "    # 每月开盘收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"日期\"].dt.to_period(\"M\")).agg({\n",
    "        \"开盘价\": \"first\",\n",
    "        \"收盘价\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"月份\"] = monthly_df[\"日期\"].dt.month\n",
    "    monthly_df[\"上涨\"] = monthly_df[\"收盘价\"] > monthly_df[\"开盘价\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"月份\")[\"上涨\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"月份\")[\"上涨\"].count()\n",
    "    up_probs = up_counts / total_counts\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合成一个图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    # 确保每月都有值\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol}行业各周期每月上涨次数比较\")\n",
    "plt.legend(title=\"周期长度\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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